Crosstabs MCP Server

One install provides two local MCP servers: 39 statistical tools for focused contingency-table analysis and 23 headless research workflow tools that take an agent from project creation and dataset import through tab books, coding, tracker repair, editable reports, and portable export. Individual statistical tools report whether their inference is exact, asymptotic, or simulated.

Features

Core Statistical Tests

Effect Sizes & Measures

Ordinal Measures

Agreement & Reliability

Advanced Analysis

Installation

From PyPI (recommended)

pip install crosstabs

Inspect the public source distribution

python -m pip download --no-deps --no-binary=:all: crosstabs

The development repository is currently private. PyPI publishes the package's source archive; email support@crosstabs.com for issue reports or source-access questions.

Quick Start

Run the MCP Server

# Focused statistical calculators (Python 3.10+)
crosstabs

# End-to-end research workspace (Python 3.10+ and Node.js 20+)
crosstabs-headless

Or directly:

python -m crosstabs_mcp.server

Configure an MCP client

Add to your ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "crosstabs_workspace": {
      "command": "crosstabs-headless"
    },
    "crosstabs_statistics": {
      "command": "crosstabs"
    }
  }
}

The headless server stores project state and generated artifacts under the local application-data directory. Deterministic calculations stay on the machine. code_open_ends is the only current operation that can use Vercel AI Gateway; it requires an explicit external-processing approval in the tool call.

Headless workflow tools

create_project, import_dataset, profile_dataset, define_row_set, define_banner, apply_filter, set_weight, run_table, run_tab_book, compare_waves, code_open_ends, review_themes, approve_coding, undo_change, replace_dataset, detect_schema_drift, repair_schema, generate_report_pack, refresh_report_pack, export_project, list_projects, inspect_project, and get_audit_history.

Mutations use expected revisions and idempotency keys. Results include structured warnings, evidence IDs, audit records, and MCP resources for generated files.

Usage Examples

Once configured, Claude can perform statistical analysis:

Chi-square Test

User: Test if there's an association between treatment and outcome:
      Treatment A: 50 success, 30 failure
      Treatment B: 20 success, 40 failure

Claude: [Uses chi_square_test with matrix [[50,30],[20,40]]]
        χ² = 11.67, p = 0.0006
        Cramér's V = 0.29 (small-medium effect)
        There is a significant association between treatment and outcome.

Odds Ratio

User: Compare an adverse outcome between exposure groups:
      Exposed: 30 outcome-present, 70 outcome-absent
      Unexposed: 15 outcome-present, 85 outcome-absent

Claude: [Uses odds_ratio with matrix [[30,70],[15,85]]]
        OR = 2.43 (95% CI: 1.21-4.87)
        Exposure is associated with 143% higher odds of the outcome.

The epidemiology tools (odds_ratio, relative_risk, risk_difference, and attributable_risk) use one explicit orientation: [[exposed outcome+, exposed outcome-], [unexposed outcome+, unexposed outcome-]]. They return machine-readable zero/infinite/undefined states rather than silently continuity-correcting point estimates. Attributable/prevented fractions require causal identification assumptions; an association alone does not establish the counterfactual effect of removing an exposure.

Fisher's Exact Test

User: I have a small sample: [[3,1],[1,5]]. Is it significant?

Claude: [Uses fishers_exact with the matrix]
        p = 0.190476 (two-tailed exact)
        Not statistically significant at α=0.05.

Available Tools

Development

Run Tests

pip install -e ".[dev]"
pytest tests/ -v

Project Structure

mcp-server-python/
├── crosstabs_mcp/
│   ├── __init__.py
│   ├── headless_launcher.py # Node version check and bundled-server launcher
│   ├── headless-mcp.mjs     # Local-first 23-tool workflow MCP server
│   ├── server.py          # Main MCP server
│   └── advanced_stats.py  # Compatibility imports; math lives in server.py
├── tests/
│   ├── test_statistics.py       # Statistical behavior tests
│   └── test_reference_parity.py # Public SciPy reference parity
├── scripts/                    # Distribution verification
├── LICENSE
├── pyproject.toml
├── uv.lock
└── README.md

Requirements

• Python 3.10+
• mcp >= 1.0.0
• fastmcp >= 0.1.0
• numpy >= 1.24.0
• scipy >= 1.10.0
• pandas >= 2.0.0
• statsmodels >= 0.14.0

License

MIT License - see LICENSE for details.

Contributing

The development repository is currently private. Send corrections and proposed changes to support@crosstabs.com.

Links

• PyPI package and source archive
• Web Application
• Support
• MCP Documentation